An automated three-dimensional multi-objective optimization and data mining method is presented by integrating a self-adaptive multi-objective differential evolution algorithm (SMODE), 3D parameterization method for blade profile and meridional channel, Reynolds-averaged Navier–Stokes (RANS) solver technique and data mining technique of self-organizing map (SOM). Using this method, redesign of a high pressure ratio centrifugal impeller is conducted. After optimization, 16 optimal Pareto solutions are obtained. Detailed aerodynamic analysis indicates that the aerodynamic performance of the optimal Pareto solutions is greatly improved. By SOM-based data mining on optimized solutions, the interactions among objective functions and significant design variables are analyzed. The mechanism behind parameter interactions is also analyzed by comparing the data mining results with the performance of typical designs.
Multi-Objective Aerodynamic Optimization Design and Data Mining of a High Pressure Ratio Centrifugal Impeller
Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received January 5, 2015; final manuscript received February 5, 2015; published online March 17, 2015. Editor: David Wisler.
Guo, Z., Song, L., Zhou, Z., Li, J., and Feng, Z. (September 1, 2015). "Multi-Objective Aerodynamic Optimization Design and Data Mining of a High Pressure Ratio Centrifugal Impeller." ASME. J. Eng. Gas Turbines Power. September 2015; 137(9): 092602. https://doi.org/10.1115/1.4029882
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